Population-wide measures due to the COVID-19 pandemic and exposome changes in the general population of Cyprus in March–May 2020

Non-pharmacological interventions (e.g., stay-at-home orders, school closures, physical distancing) implemented during the COVID-19 pandemic are expected to have modified routines and lifestyles, eventually impacting key exposome parameters, including, among others, physical activity, diet and cleaning habits. The objectives were to describe the exposomic profile of the general Cypriot population and compliance to the population-wide measures implemented during March–May 2020 to lower the risk of SARS-CoV-2 transmission, and to simulate the population-wide measures’ effect on social contacts and SARS-CoV-2 spread. A survey was conducted in March–May 2020 capturing different exposome parameters, e.g., individual characteristics, lifestyle/habits, time spent and contacts at home/work/elsewhere. We described the exposome parameters and their correlations. In an exposome-wide association analysis, we used the number of hours spent at home as an indicator of compliance to the measures. We generated synthetic human proximity networks, before and during the measures using the dynamic-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbb{S}}$$\end{document}S1 model and simulated SARS-CoV-2 transmission (i.e., to identify possible places where higher transmission/number of cases could originate from) on the networks with a dynamic Susceptible-Exposed-Infectious-Recovered model. Overall, 594 respondents were included in the analysis (mean age 45.7 years, > 50% in very good health and communicating daily with friends/family via phone/online). The median number of contacts at home and at work decreased during the measures (from 3 to 2 and from 12 to 0, respectively) and the hours spent at home increased, indicating compliance with the measures. Increased time spent at home during the measures was associated with time spent at work before the measures (β= -0.87, 95% CI [-1.21,-0.53]) as well as with being retired vs employed (β= 2.32, 95% CI [1.70, 2.93]). The temporal network analysis indicated that most cases originated at work, while the synthetic human proximity networks adequately reproduced the observed SARS-CoV-2 spread. Exposome approaches (i.e., holistic characterization of the spatiotemporal variation of multiple exposures) would aid the comprehensive description of population-wide measures’ impact and explore how behaviors and networks may shape SARS-CoV-2 transmission. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-14468-z.

Population estimates for 2019 per district, sex and age group for the population in the government-controlled areas of the Republic of Cyprus according to the Cyprus Statistical Services used in weighting the study population (Source: https://www.cystat.gov.cy/el/PublicationList?s=46).        (6) 18 (5) 20 (9) 4-6 times per week

Figure S 3
Dynamic SEIR simulation results on the synthetic human proximity networks generated with the dynamic-S1 model using the non-normalized number of contacts (i.e. raw number of contacts reported by the study respondents). Top row: total cases, daily new cases and active cases, averaged over 50 dynamic SEIR processes for R0=2.58. Middle and bottom rows show the same results as the top row but for R0=3.26 and R0=4.01, respectively. The shaded area in each plot corresponds to one standard deviation away from the average. The vertical red line marks the beginning of the measures.

Data collection
Questions from five validated questionnaires and a diary were used for the design of the selected questions for this proposed study, so that reliability and validity of the questionnaire are established: 1. Diary that documents physical and non-physical contacts, location and duration of each contact (1). 2. European Health Interview Survey for 2014 based on the Cyprus Statistical Services (2) 3. European Urban Health Indicators survey (3) The dynamic-1 model The dynamic-1 model is based on the 1 model of traditional complex networks (7,8) and has been recently shown to reproduce many of the observed structural and dynamical properties of human proximity networks (9). The dynamic-1 models a sequence of network snapshots, G t , t = 1, . . . , τ . Each snapshot is a realization of the 1 model. Thus, each node in the model has latent variables κ and θ, which remain fixed in all snapshots. The latent variable κ is the node's hidden degree per time slot, proportional to its expected degree (number of contacts) in each snapshot. Thus, κ abstracts the popularity of the node in the population. The latent variable θ is the angular (similarity) coordinate of the node in a circle of radius R = N/2π, where N is the total number of nodes. The angular distance ∆θij = π − |π − |θi − θj|| between nodes i, j abstracts their similarity. Each snapshot G t is allowed to have a different average degree k ̅ t , t = 1, . . . , τ . The model also has a network temperature parameter T that takes values in (0, 1). The temperature T plays a central role in network dynamics in the model, dictating the distributions of contact and intercontact durations, the node degrees, among others (9).
To generate a network with N nodes, τ snapshots, snapshot degrees k ̅ t , t = 1, . . . , τ and T ∈ (0, 1) we do the following: First, for each node i = 1, 2, . . . , N sample its angular coordinate θ i uniformly at random from [0, 2π], and its degree variable κ i from a probability density function ρ(κ). Then, snapshots are generated according to the following simple rules:Fsha 1. at each time step t = 1, . . . , τ , snapshot G t starts with N disconnected nodes.
2. each pair of nodes i, j connects with probability: In this expression, χ ij is the effective distance between nodes i and j, where parameter µ is derived from the condition that the expected degree in the snapshot is indeed k ̅ t yielding μ = k ̅ t sin(Tπ) 2κ ̅ 2 Tπ , where κ ̅ = ∫ κρ(κ)dκ.
3. at time t + 1, all edges in snapshot G t are deleted and the process starts over again to generate snapshot G t+1 .
We note that smaller values of T increase (decrease) the connection probability among nodes at smaller (larger) effective distances.

Constructing synthetic human proximity networks from the Exposome@home survey
To generate the temporal network for a single setting (workplace, elsewhere or home) before or during the measures with the dynamic-1 , the procedure is as follows. First, we assume that all considered 578 respondents are present in all settings, therefore we set the number of nodes N = 578 in all cases.
The number of time slots τ is set according to the average number of hours spent in the setting in a day reported by the respondents. We assume that the network time-slots have a duration of five minutes, giving τ = 84 (7 hours infector-the serial interval- (10). For each infected node i in the simulations, we find the number of days s that elapsed between the day i transitioned to the infected compartment (onset of symptoms) and the day when the node that infected i transitioned to the infected compartment. Having found all serial intervals s in the simulations we compute the empirical distribution of serial intervals, from which we obtain the probabilities w s . IRB approval Mention whether the study has been approved by an IRB. The study was approved by the Cyprus National Bioethics

Informed consent
Describe the informed consent process. Where were the participants told the length of time of the survey, which data were stored and where and for how long, who the investigator was, and the purpose of the study?
Respondents were informed about length of time of the survey, the duration of data storage, the responsible investigator, and the purpose of the study on the cover page of the questionnaire. Also, parents were informed that the data collection was anonymous and that, if they wished to, they would withdraw from the study at any time during survey completion step.

Data protection
If any personal information was collected or stored, describe what mechanisms were used to protect unauthorized access.
Respondents were informed that all collected data will be used for statistical analysis along without showing any personal data. Only the research team has access to the data using a password-based database at the Cyprus International Institute for Environmental and Public Health.

Not applicable
Statistical correction Indicate whether any methods such as weighting of items or propensity scores have been used to adjust for the nonrepresentative sample; if so, please describe the methods.
To account for differences between the study population and the Cypriot population distribution by age, sex, and geographical district, as well as to allow for extrapolation of the study estimates to the Cypriot population, we weighted the survey population using the raking method. To calculate the weights, we used the most recent (2019) population age and sex estimates by each geographical district available by the Statistical Service of Cyprus.